(1) Technical Field
The present invention relates to general techniques for computer vision and object classification. More specifically, the present invention relates to use of constrained disparity with stereoscopic images to generate three-dimensional shape estimates.
(2) Discussion
Matching features in images of a scene taken from two different viewpoints (stereoscopic images) is a major problem in the art of machine vision systems. A variety of constraint-based solutions have been proposed, and have met with varying degrees of success because there is no general solution to the problem and a set of constraints applied to one scene may not be appropriate for other scenes.
In particular, a three-dimensional scene is reduced to two-dimensional images when captured by an imaging device. Thus, the pictures each contain less information than the original scene. In the art of stereoscopic imaging, a pair of two-dimensional images, each taken from a different location, is used to approximate the information from the original three-dimensional scene. However, in attempting to reconstruct the original three-dimensional scene, another problem arises—that of identifying corresponding points in the pair of images. In other words, for any individual pixel in one image, there are many potential corresponding pixels in the other image. Thus, a major difficulty in the art is determining corresponding pixels in the images so that disparities between the images may be determined in order to approximate the original three-dimensional scene.
Accordingly, there exists a need in the art for a fast and reliable system for approximating a three-dimensional scene from stereoscopic images. The present invention provides such a system, using a texture filter to generate a disparity estimate and refining the disparity estimate iteratively using disparity constraints until a final estimate is achieved.